scholarly journals A framework for evaluating correspondence between brain images using anatomical fiducials

2018 ◽  
Author(s):  
Jonathan C. Lau ◽  
Andrew G. Parrent ◽  
John Demarco ◽  
Geetika Gupta ◽  
Jason Kai ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Clinical Applications ◽  
Quantitative Approach ◽  
Stereotactic Neurosurgery ◽  
Brain Images ◽  
Crucial Step ◽  
Individual Participant ◽  
Novice Users

AbstractAccurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.

scholarly journals Cognitive States from Brain Images : SVM Approach

2012 ◽  
pp. 139-144
Author(s):  
Ramakrushna Swain ◽  
Lambodar Jena ◽  
Narendra K. Kamila
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Temporal Correlation ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Brain Images ◽  
Cognitive States ◽  
Fmri Study ◽  
New Instrument ◽  
Spatio Temporal

As the field of functional human brain mapping has matured, it has become apparent that a comprehensive understanding of the human brain, and its relationship with cognition, will require a quantitative assessment of individual differences in both brain function and structure. To assess brain structure, accurate classification of magnetic resonance images needed. In recent years, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. As in other modern empirical sciences, this new instrumentation has led to a flood of new data and a corresponding need for new data analysis methods. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. In this paper we discuss the analysis of fMRI data, from the angle of support vector machine classification for analysis of complex, multivariate data.

Lipsia—a new software system for the evaluation of functional magnetic resonance images of the human brain

2001 ◽  
Vol 25 (6) ◽  
pp. 449-457 ◽  
Author(s):  
Gabriele Lohmann ◽  
Karsten Müller ◽  
Volker Bosch ◽  
Heiko Mentzel ◽  
Sven Hessler ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Software System ◽  
Functional Magnetic Resonance

scholarly journals Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images

2021 ◽  
Vol 20 (3) ◽  
pp. 356-363
Author(s):  
V. Yegnanarayanan ◽  
◽  
M. Anisha ◽  
T. Arun Prasath ◽  
◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Bipartite Graph ◽  
General Procedure ◽  
Brain Network ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Brain Images ◽  
Cad System ◽  
Graph Modeling

This paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain networks and a general procedure to generate a graph from the AD brain network. Further, as AD is a minacious disorder that leads to the progressive decline of memory and physical ability we resort to Computer-Aided Diagnosis. It has a vital part in the preliminary estimation and finding of AD. We propose an approach to become aware of AD particularly in its beginning phase by analyzing the measurable variations in the hippocampus, grey matter, cerebrospinal fluid and white matter of the brain from Magnetic resonance images. Hence an appropriate segmentation and categorization methods are projected to detect the presence of AD. The trials were carried out on Magnetic resonance images to distinguish from the section of interest. The effectiveness of the CAD system was experimentally evaluated from the images considered from publicly available databases. Obtained findings recommend that the established CAD system has boundless prospective and great guarantee for the prognosis of AD.

scholarly journals Experimental Hypoxemic Hypoxia: Effects of Variation in Hematocrit on Magnetic Resonance T2*-Weighted Brain Images

1998 ◽  
Vol 18 (9) ◽  
pp. 1018-1021 ◽  
Author(s):  
Weili Lin ◽  
Richard P. Paczynski ◽  
Azim Celik ◽  
Chung Y. Hsu ◽  
William J. Powers
Keyword(s):  
Magnetic Resonance ◽  
Oxygen Saturation ◽  
Hemoglobin Concentration ◽  
Theoretical Models ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Blood Oxygen Saturation ◽  
Brain Images ◽  
Weighted Magnetic Resonance Imaging ◽  
Linear Regressions

T2*-weighted gradient echo magnetic resonance images of rat brain were obtained dynamically during acute hypoxemic hypoxia to investigate the relations between changes in cerebral blood oxygen saturation(ΔYb), blood hematocrit (Hct), and R2* (ΔR2*). Images from hypoxemic rats with normal Hct (42.8% ± 2.33%; n = 12) were compared with those from hypoxemic rats with mild (33.4% ± 1.88%; n = 8) or moderate (27.14% ± 2.7%; n = 10) reduction of Hct. A linear relation between ΔYb and ΔR2* was obtained for all three groups. However, the slopes of the linear regressions were statistically different from one another ( P < 0.001), with the slopes of the regression lines increasing inversely with Hct; that is, the slope for normal Hct is less than the slope for mildly reduced Hct, which is less than the slope for moderately reduced Hct. These data suggest that for any given reduction in the oxygen saturation of cerebral blood, the ΔR2* will be of a lesser magnitude when the hemoglobin concentration is reduced; the data are consistent with existing theoretical models of deoxyhemoglobin content-dependent effects in T2*-weighted magnetic resonance imaging.

AN INTERACTIVE CBIR SYSTEM BASED ON ANFIS LEARNING SCHEME FOR HUMAN BRAIN MAGNETIC RESONANCE IMAGES RETRIEVAL

2012 ◽  
Vol 24 (01) ◽  
pp. 27-36 ◽  
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Image Retrieval ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Query Image ◽  
Fuzzy Classifier ◽  
Inference System

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

On the problem of geometric distortion in magnetic resonance images for stereotactic neurosurgery

1994 ◽  
Vol 12 (5) ◽  
pp. 749-765 ◽  
Author(s):  
J. Michiels ◽  
H. Bosmans ◽  
P. Pelgrims ◽  
D. Vandermeulen ◽  
J. Gybels ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Geometric Distortion ◽  
Stereotactic Neurosurgery
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